WMMSE-Based Alternating Optimization for Low-Complexity Multi-IRS MIMO Communication

Recently, intelligent reflecting surface (IRS) has emerged as a promising cost-efficient technology to enhance communication performance. In this paper, we start with a single-user IRS-assisted multiple-input-multiple-output (MIMO) system, aiming to maximize spectral efficiency. To deal with the complicated non-convex problem, we exploit the equivalence between the weighted mean square error minimization (WMMSE) and the spectral efficiency maximization problem, and propose a low-complexity and low-latency WMMSE-based alternating optimization (WMMSE-AO). In addition, it can be executed in parallel for further speedup of the IRS computation due to its non-coupling characteristic. Moreover, to fully exploit spatial multiplexing, we extend our proposed WMMSE-AO to general multi-IRS systems with better performance and coverage. Simulation results show that the proposed WMMSE-AO can reduce 20 times multiplications with only 1% performance degradation compared with the state-of-the-art algorithm.